
!pip install folium

Requirement already satisfied: folium in c:\users\dell\anaconda3\lib\site-packages (0.12.1)
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Requirement already satisfied: idna<3,>=2.5 in c:\users\dell\anaconda3\lib\site-packages (from requests->folium) (2.10)


!pip install plotly

Requirement already satisfied: plotly in c:\users\dell\anaconda3\lib\site-packages (4.14.3)
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import plotly.express as px


import plotly.express as px


import plotly.figure_factory as ff


from plotly.subplots import make_subplots


import folium


import plotly.graph_objects as go


import pandas as pd


import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

%matplotlib inline

import math
import random
from datetime import timedelta


cnf = '#393e46'
dth = '#ff2e63'
rec = '#21bf73'
act = '#fe9801'


import warnings


warnings.filterwarnings('ignore')


import plotly as py
py.offline.init_notebook_mode(connected = True)


import os


df = pd.read_csv('cse.csv', encoding = 'latin')


df.head()


df.isna().sum()

SR Case Number               0
CES Date Created             0
Region                       0
Country Name                 0
Customer Effort Score        0
CES Why                      0
CES Suggestion               0
Domain Metier                0
SR Category                  0
SR Type                      0
SR Product Category          0
Account Name                 0
AccountOwner                 0
SR Contact Name              0
SR Severity                  0
Date Creation                0
Year Creation                0
Month Creation               0
Date Resolution              0
Date Closed                  0
Closing days                 0
Group                        0
CaseOwner                    0
SLA Violation                0
Age with Pending-External    0
dtype: int64


df['SR Product Category'].fillna(0, inplace=True)


confirmed = df.groupby('Date Resolution').sum()['Age with Pending-External'].reset_index()


confirmed.head(20)


confirmed1 = df.groupby('Date Resolution').sum()['Closing days'].reset_index()


confirmed1.head(20)


fig = go.Figure()
fig.add_trace(go.Scatter(x= df['Closing days'], y = df['Date Resolution']))
fig.show() # mostly case closing days are mostly before 10 days


fig = go.Figure()
fig.add_trace(go.Scatter(x= df['Closing days'], y = df['CaseOwner']))
fig.show() # mostly case closing days are mostly before 10 days


fig = go.Figure()
fig.add_trace(go.Scatter(x= df['Closing days'], y = df['SR Type']))
fig.show() # mostly case closing days are mostly before 10 days


temp = df.groupby('CaseOwner')['Closing days','Customer Effort Score','SR Category'].sum()


temp


rate = df.groupby('Date Resolution').sum()


rate


df1 = pd.read_csv('tap.csv', encoding = 'latin')


df1.head()


# Customer Effort Score Year 7 -very strongly agree and 1 very strongly disagree


df['CaseOwner'].value_counts().head(10).plot.bar()

<AxesSubplot:>


df['CaseOwner'].value_counts()

Generic WELCOME      147
David DARASSE         86
Audrey BIEBER         55
Christine WANDREY     34
Irene MORIANO         32
                    ... 
Sauveur BARUCH         1
Marc BEAURAIN          1
Jonathan Dudley        1
Frederic LEMMEL        1
Pan YI                 1
Name: CaseOwner, Length: 262, dtype: int64


df.columns

Index(['SR Case Number', 'CES Date Created', 'Region', 'Country Name',
       'Customer Effort Score', 'CES Why', 'CES Suggestion', 'Domain Metier',
       'SR Category', 'SR Type', 'SR Product Category', 'Account Name',
       'AccountOwner', 'SR Contact Name', 'SR Severity', 'Date Creation',
       'Year Creation', 'Month Creation', 'Date Resolution', 'Date Closed',
       'Closing days', 'Group', 'CaseOwner', 'SLA Violation',
       'Age with Pending-External'],
      dtype='object')


fig = px.bar(df, x= 'CaseOwner', y = 'Country Name', color = 'CaseOwner', height = 600,
            title = 'caseowners vs country ', color_discrete_sequence= px.colors.cyclical.mygbm)
fig.show()


fig = px.bar(df, x= 'CaseOwner', y = 'Date Resolution', color = 'Date Resolution', height = 600,
            title = 'caseowners vs Date Resolution', color_discrete_sequence= px.colors.cyclical.mygbm)
fig.show()


fig = px.bar(df, x= 'CaseOwner', y = 'Date Resolution', color = 'Date Resolution', height = 600,
            title = 'caseowners vs Date Resolution', color_discrete_sequence= px.colors.cyclical.mygbm)
fig.show()


fig = px.line(df, x= 'CaseOwner', y = 'Date Resolution', color = 'Date Resolution', height = 600,
            title = 'caseowners vs Date Resolution', color_discrete_sequence= px.colors.cyclical.mygbm)
fig.show()


# Top 30 Case owners


df= pd.read_csv('product.csv')


df.head(5)


import pandas as pd
import seaborn as sns   # Why sns?  It's a reference to The West Wing
import matplotlib.pyplot as plt  # seaborn is based on matplotlib
sns.set(color_codes=True) # adds a nice background to the graphs
%matplotlib inline 
# tells python to actually display the graphs


df.head(40)


df= pd.read_csv('sev.csv')


df.head(40)

	SR Case Number	CES Date Created	Region	Country Name	Customer Effort Score	CES Why	CES Suggestion	Domain Metier	SR Category	SR Type	...	Date Creation	Year Creation	Month Creation	Date Resolution	Date Closed	Closing days	Group	CaseOwner	SLA Violation	Age with Pending-External
0	497539	16/10/2020	North America	USA	1	I refuse to provide surveys for Alcatel until ...	refuse to provide surveys for Alcatel until th...	TECHNICAL SUPPORT COMMUNICATIONS	Technical Support Communications	Product Support	...	29/09/2020	2020	10	06/10/2020	17/10/2020	11	LARGE SYSTEM TAC ENGLISH	Venkatesh GOPALRAO	0	7
1	496273	16/10/2020	Europe South	Lebanon	7	NaN	NaN	TECHNICAL SUPPORT COMMUNICATIONS	Technical Support Communications	Product Support	...	24/09/2020	2020	10	15/10/2020	16/10/2020	1	LARGE SYSTEM TAC ENGLISH	Vijaya Marthandan	1	21
2	500170	16/10/2020	Europe North	United Kingdom	7	The ALE rep understood and action the requirment.	none.	--	License Management	License Request	...	12/10/2020	2020	10	14/10/2020	16/10/2020	2	OTEC_TAC	Gunasekaran MANI	1	2
3	501086	16/10/2020	Europe North	United Kingdom	7	As they processed the ESR within a responble t...	None.	TOOLS	License Management	License Request	...	15/10/2020	2020	10	16/10/2020	16/10/2020	0	E-LICENSING & ORDERING SUPPORT	David DARASSE	0	1
4	500777	16/10/2020	Europe North	Germany	7	NaN	NaN	TECHNICAL SUPPORT NETWORK	Technical Support Network	Product Support	...	14/10/2020	2020	10	15/10/2020	15/10/2020	0	NETWORK SOLUTIONS TEC	Devender DHIMAN	0	1

	Date Resolution	Age with Pending-External
0	01/04/2020	74
1	01/05/2020	1
2	01/06/2020	24
3	01/07/2020	253
4	01/09/2020	14
5	01/10/2019	256
6	01/10/2020	3
7	01/11/2019	11
8	02/01/2020	223
9	02/03/2020	49
10	02/04/2020	39
11	02/06/2020	80
12	02/07/2019	56
13	02/07/2020	2
14	02/08/2019	194
15	02/09/2019	7
16	02/09/2020	22
17	02/10/2019	113
18	02/10/2020	9
19	02/12/2019	87

	Date Resolution	Closing days
0	01/04/2020	63
1	01/05/2020	0
2	01/06/2020	5
3	01/07/2020	47
4	01/09/2020	1
5	01/10/2019	3
6	01/10/2020	10
7	01/11/2019	4
8	02/01/2020	21
9	02/03/2020	28
10	02/04/2020	81
11	02/06/2020	21
12	02/07/2019	3
13	02/07/2020	11
14	02/08/2019	7
15	02/09/2019	1
16	02/09/2020	0
17	02/10/2019	1
18	02/10/2020	13
19	02/12/2019	4

	Closing days	Customer Effort Score
CaseOwner
Abdelhaq MEJDOUB	116	6
Agata KULA	36	132
Aijaz Ahmad ITOO	0	13
Ajith VALLEPARAMBILSURENDRAN	0	25
Akshay NAROTTAM VARIA	14	48
...	...	...
Viviane BUDALY	5	7
Yannick GUILLEMOT	5	6
Yasir ATHIKKAL PANDIKASALAYIL	27	62
Yogendran KUPPUSWAMY	0	70
Zuzanna SZYMCZAK	0	7

	SR Case Number	Customer Effort Score	Year Creation	Month Creation	Closing days	Age with Pending-External
Date Resolution
01/04/2020	3205620	48	14140	29	63	74
01/05/2020	466317	7	2020	5	0	1
01/06/2020	940363	14	4040	12	5	24
01/07/2020	4258278	52	18180	63	47	253
01/09/2020	1962700	28	8080	36	1	14
...	...	...	...	...	...	...
31/05/2020	465863	6	2020	6	5	33
31/07/2019	1567900	27	8076	29	1	203
31/07/2020	2897848	41	12120	45	13	73
31/08/2020	1947372	28	8080	32	0	86
31/10/2019	1694409	27	8076	40	0	44

	Case _owner	Very strong Disagree	Strongly Disagree	Disagree	Agree or Disagree	Agree	Strongly Agree	Very strongly Agree	Unnamed: 8	Unnamed: 9
0	David DARASSE	1.0	0.0	0.0	3.0	2.0	10.0	70.0	NaN	NaN
1	NaN	1.0	NaN	NaN	3.0	1.0	18.0	58.0	NaN	NaN
2	NaN	2.0	3.0	1.0	6.0	3.0	19.0	48.0	NaN	NaN
3	NaN	1.0	2.0	1.0	4.0	2.0	12.0	47.0	NaN	NaN
4	Audrey BIEBER	0.0	1.0	0.0	3.0	1.0	8.0	42.0	NaN	NaN

	caseowner	products handled
0	Generic WELCOME	140
1	David DARASSE	83
2	Audrey BIEBER	53
3	Christine WANDREY	34
4	Irene MORIANO	32

	Case owner	SRSeverity	Unnamed: 2	Unnamed: 3	Unnamed: 4	Unnamed: 5	Total
0	NaN	NaN	S1-Critical	S2-High	S3-Medium	S4-Low	NaN
1	Abdelhaq MEJDOUB	0.0	0	0	0	1	1.0
2	Agata KULA	0.0	0	16	1	1	18.0
3	Aijaz Ahmad ITOO	0.0	0	0	0	1	1.0
4	Ajith VALLEPARAMBILSURENDRAN	0.0	0	0	0	4	4.0
5	Akshay NAROTTAM VARIA	0.0	0	0	1	6	7.0
6	Alain BOTTI	0.0	0	0	2	1	3.0
7	Albert DHANARAJ	0.0	0	0	0	1	1.0
8	Alice ROUSSELOT	0.0	0	0	4	9	13.0
9	Amar HARPALANI	0.0	0	0	2	8	10.0
10	Ambalatharasan GNANAPRAKASAM	0.0	0	0	1	5	6.0
11	Ana VENEGAS	0.0	0	1	0	0	1.0
12	Andre JARNOUX	0.0	0	0	0	1	1.0
13	Ankush Keche	0.0	0	0	0	2	2.0
14	Anne PELLETIER	0.0	0	0	0	4	4.0
15	Annie ARMANDO	0.0	1	0	0	0	1.0
16	Anu ULAGANATHAN	0.0	0	0	1	0	1.0
17	Aravindan MANOHAR	0.0	0	0	6	15	21.0
18	Arvind DOWLUT	0.0	0	0	4	11	15.0
19	Ashvin CHUMMUN	0.0	0	0	0	3	3.0
20	Athirai AJEETHKUMARMANJULA	0.0	0	0	3	23	26.0
21	Audrey BIEBER	0.0	2	47	2	2	53.0
22	Aurelie PARISET	0.0	0	5	1	0	6.0
23	Aurelie Schmoderer	0.0	0	0	0	2	2.0
24	Aurelien CLARISSE	0.0	0	0	2	7	9.0
25	Ayoub EL-HABHOUB	0.0	0	0	1	0	1.0
26	Badr Eddine ATTALBI ALAMI	0.0	0	0	2	0	2.0
27	Bala CHANDRASEKHAR	0.0	0	0	3	5	8.0
28	Balaji TANDABANY	0.0	0	0	4	7	11.0

	caseowner	products handled
0	Generic WELCOME	140
1	David DARASSE	83
2	Audrey BIEBER	53
3	Christine WANDREY	34
4	Irene MORIANO	32
5	Jegen DORASAMI	29
6	Sandrine JUNG	27
7	Athirai AJEETHKUMARMANJULA	26
8	Paari PANNEERSELVAM	25
9	Parash PAGOOAH	23
10	Gunasekaran MANI	22
11	Katarina STADLER	22
12	Aravindan MANOHAR	21
13	Hislamar BRACHO	20
14	Kesaw KUMAR	19
15	Agata KULA	18
16	Bruno FLOCH	18
17	Marc BERLING	18
18	Stephane LE MEN	18
19	Monisha RAJENDRAN	17
20	Muhammad UZAIR MAMOOJEE	17
21	Samuel CHANDRASEKARAN	17
22	Boobalan SUBRAMANI	16
23	Kishan SIVADAS	16
24	Arvind DOWLUT	15
25	Iyyappan LAKSHMANAN	15
26	Neethu Chenthamarai	15
27	Ramakrishna Kalagara	15
28	Luis Rosales	14